Added Chatbot that replies to everything related to blockchain

chat-bot/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
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+pip-log.txt
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+# Unit test / coverage reports
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+.tox/
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+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
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+
+# Django stuff:
+*.log
+local_settings.py
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+# Flask stuff:
+instance/
+.webassets-cache
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+.scrapy
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+# Sphinx documentation
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+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+__pypackages__/
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+celerybeat-schedule
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+
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+*.sage.py
+
+# Environments
+.env
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+env/
+venv/
+ENV/
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+# Spyder project settings
+.spyderproject
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+# mkdocs documentation
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+# mypy
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+dmypy.json
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+# Pyre type checker
+.pyre/
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+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+/venv

chat-bot/README.md

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+## Installation
+
+### Create an environment using venv
+```console
+$ cd chat-bot
+$ python3 -m venv venv
+```
+
+### Activate it
+Mac / Linux:
+```console
+. venv/bin/activate
+```
+Windows:
+```console
+venv\Scripts\activate
+```
+### Install PyTorch and dependencies
+
+For Installation of PyTorch see [official website](https://pytorch.org/).
+
+You also need `nltk`:
+ ```console
+pip install nltk flask_cors numpy torch flask
+ ```
+
+If you get an error during the first run, you also need to install `nltk.tokenize.punkt`:
+Run this once in your terminal:
+ ```console
+$ python
+>>> import nltk
+>>> nltk.download('punkt')
+```
+
+## Usage
+Run
+```console
+python train.py
+```
+This will dump `data.pth` file. And then run
+```console
+python app.py
+```
+
+## Customize
+Have a look at [intents.json](intents.json). You can customize it according to your own use case. Just define a new `tag`, possible `patterns`, and possible `responses` for the chat bot. You have to re-run the training whenever this file is modified.
+```console
+{
+  "intents": [
+    {
+      "tag": "greeting",
+      "patterns": [
+        "Hi",
+        "Hey",
+        "How are you",
+        "Is anyone there?",
+        "Hello",
+        "Good day"
+      ],
+      "responses": [
+        "Hey :-)",
+        "Hello, thanks for visiting",
+        "Hi there, what can I do for you?",
+        "Hi there, how can I help?"
+      ]
+    },
+    ...
+  ]
+}
+```

chat-bot/app.py

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+import random
+import json
+from flask import Flask, request, jsonify
+
+import torch
+from model import NeuralNet
+from nltk_utils import bag_of_words, tokenize
+from flask_cors import CORS 
+
+app = Flask(__name__)
+CORS(app) 
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+with open('intents.json', 'r') as json_data:
+    intents = json.load(json_data)
+
+FILE = "data.pth"
+data = torch.load(FILE)
+
+input_size = data["input_size"]
+hidden_size = data["hidden_size"]
+output_size = data["output_size"]
+all_words = data['all_words']
+tags = data['tags']
+model_state = data["model_state"]
+
+model = NeuralNet(input_size, hidden_size, output_size).to(device)
+model.load_state_dict(model_state)
+model.eval()
+
+bot_name = "Agora"
+
+@app.route('/api/chat', methods=['POST'])
+def chat():
+    try:
+        data = request.get_json()
+        user_message = data['message']
+
+        # Your existing chatbot logic
+        sentence = tokenize(user_message)
+        X = bag_of_words(sentence, all_words)
+        X = X.reshape(1, X.shape[0])
+        X = torch.from_numpy(X).to(device)
+
+        output = model(X)
+        _, predicted = torch.max(output, dim=1)
+
+        tag = tags[predicted.item()]
+
+        probs = torch.softmax(output, dim=1)
+        prob = probs[0][predicted.item()]
+
+        if prob.item() > 0.75:
+            for intent in intents['intents']:
+                if tag == intent["tag"]:
+                    bot_response = random.choice(intent['responses'])
+        else:
+            bot_response = "I do not understand..."
+
+        return jsonify({"message": bot_response})
+
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+if __name__ == '__main__':
+    app.run(debug=True)